Natural Language

Insightful text analysis

Natural Language uses machine learning to reveal the structure and meaning of text.
You can extract information about people, places, and events, and better understand
social media sentiment and customer conversations. Natural Language enables you to
analyze text and also integrate it with your document storage on Cloud
Storage.

AutoML Natural Language

Train your own high-quality machine learning custom models to classify, extract, and
detect sentiment with minimum effort and machine learning expertise using
AutoML Natural Language. You can use the
AutoML Natural Language UI to upload your training data and then test your custom model.
Use AutoML Natural Language to extract information from a range of content, such as
collections of articles, scanned PDFs, or previously archived records.

Natural Language API demo

How AutoML Natural Language works

Benefits

Insights from customers

Use entity analysis to find and label fields within a document — including emails,
chat, and social media — and then sentiment analysis to understand customer opinions
to find actionable product and UX insights.

Multimedia and multilingual support

Combine Natural Language with our Speech-to-Text
API to extract insights from audio conversations. Use it with optical character
recognition (OCR) in our Vision API to
understand scanned documents. Extract entities and understand sentiments in multiple
languages with our Translation API.

Extract key document entities that matter

Use custom entity extraction to identify domain-specific entities within
documents — many of which don’t appear in standard language models —
without having to spend time or money on manual analysis.

Receipt and invoice understanding

Entity extraction can identify common entries in receipts and invoices — dates,
phone numbers, companies, prices, and so on — to help you understand the
relationships between a request and proof of payment. It even validates addresses
with Google Maps.

Content classification relationship graphs

Classify documents by common entities, domain-specific customized entities, or 700+
general categories, like sports and entertainment. Syntax analysis can help you build
relationship graphs of the entities extracted from news or Wikipedia articles.

Best of Google deep-learning models

The Natural Language API offers you the same deep machine learning technology that
powers both Google Search’s ability to answer specific user questions and the
language-understanding system behind Google Assistant.

Which Natural Language product is right for you?

You can work with either one or reap the benefits of both products by using Natural
Language API to quickly reveal the structure and meaning of text — using thousands of
pretrained classifications — and using AutoML Natural Language to classify content into
custom categories to suit your specific needs.

AutoML Natural Language

Natural Language API

Integrated REST API

Natural Language is accessible via our REST API. Text can be uploaded in
the request or integrated with Cloud Storage.

Syntax analysis

Extract tokens and sentences, identify parts of speech and create
dependency parse trees for each sentence.

Entity analysis

Identify entities within documents — including receipts, invoices, and
contracts — and label them by types such as date, person, contact
information, organization, location, events, products, and media.

Custom entity extraction

Identify entities within documents and label them based on your own domain-specific
keywords or phrases.

Sentiment analysis

Understand the overall opinion, feeling, or attitude sentiment expressed in
a block of text.

Custom sentiment analysis

Understand the overall opinion, feeling, or attitude expressed in a block of
text tuned to your own domain-specific sentiment scores.

Content classification

Classify documents in 700+ predefined categories.

Custom content classification

Create labels to customize models for unique use cases, using your own training data.

Use the structure and layout information in PDFs to improve custom entity
extraction performance.

Large dataset support

Unlock complex use cases with support for 5,000 classification labels,
1 million documents, and 10 MB document size.

Our Customers

As part of our goal to accelerate the process of doing business, we help our customers add new documents to DocuSign to get signatures and collect information. Traditionally, they would manually ‘tag’ those documents to show people where to input and where to sign... By using custom entity extraction within AutoML Natural Language, we can use large data sets to train our model and continually improve the process, no matter where the document comes from.

— Kiran Kaza, Head of Mobile Engineering, DocuSign

In the newsroom, precision and speed are critical to engaging our readers. Google Cloud Natural Language is unmatched in its accuracy for content classification. At Hearst, we publish several thousand articles a day across 30+ properties and, with natural language processing, we're able to quickly gain insight into what content is being published and how it resonates with our audiences.

The team here at Meredith is always looking for better ways to manage our content. We are looking forward to using AutoML Natural Language to apply our custom universal taxonomy to our content. AutoML Natural Language allows us to create custom models that meet our specific needs, with higher accuracy than other solutions that we considered.

— Grace Preyapongpisan, Vice President of Business Intelligence, Meredith (world-renowned brands such as Martha Stewart and Time Magazine)

Classifying Opinion and Editorials can be time-consuming and difficult work for any data science team, but Cloud Natural Language was able to instantly identify clear topics with a high-level of confidence. This tool has saved me weeks, if not months, of work to achieve a level of accuracy that may not have been possible with our in-house resources.

— Jonathan Brooks-Bartlett, Data Scientist, News UK

Through an employee stress coaching app, we helped our client use custom sentiment analysis in AutoML Natural Language to assess and analyze stress indicators and feelings in a chatbot experience. This technology enabled us to iterate through very quickly to provide an engaging and empathetic consumer experience. This will be an integral product to be used on future projects which require customised sentiment analysis, due to the speed of development and accuracy of the predictions.

— Jason Quek, CTO, Avalon Solutions

We decided to use Google Cloud’s AutoML Natural Language because it reduces overfitting with limited training samples and can scale easily to fit more document types over time. We were able to quickly deploy AutoML Natural Language for custom classification, and down the road we believe we could use the AutoML Natural Language custom entity extraction feature to help with specific use cases like contract review and mortgage data validation.